Artificial intelligence (AI) is increasingly being incorporated into engineering practices, transforming the design, construction, operation, and maintenance of systems. The present study explores recent peer-reviewed literature on three interwoven aspects of this transformation: ethical governance, societal change, and environmental sustainability. The review covers new governance instruments such as the European Union Artificial Intelligence Act, ISO/IEC 42001, and the NIST AI Risk Management Framework, as well as explainable-AI (XAI) methods that engineers are increasingly using to explain to regulators, clients, and the public why their safety-critical decisions are made. Also, it addresses the implications of AI on engineering labor markets, professional education and decision-making power, including the differential exposure of high-skill cognitive tasks to automation, and the need for new skills. Furthermore, it considers the dual nature of AI in terms of its contribution to resource optimization, predictive maintenance and integration of renewable energy, and its contribution to an increasing computational carbon and water footprint that can offset these benefits, a tension that is sometimes referred to as the Jevons paradox. The review suggests an integrated framework of responsible AI engineering that connects the three elements of responsible AI: technical explainability, institutional accountability, and life-cycle environmental auditing. The review concludes that long-term acceptability and acceptance of AI in engineering practice will not only be determined by the performance of the models themselves, but also by the ability of the engineering discipline to govern AI transparently, fairly and within planetary limits.
- Open Access
- Review
Artificial Intelligence and the Future of Engineering: A Review of Ethical Governance, Societal Transformation, and Environmental Sustainability
Author Information
Received: 20 Jun 2026 | Revised: 10 Jul 2026 | Accepted: 11 Aug 2026 | Published: 17 Aug 2026
Abstract
Graphical Abstract
Keywords
artificial intelligence | engineering ethics | AI governance | explainable AI | environmental sustainability | responsible innovation
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